{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross-walking and DisMod-MR\n",
    "\n",
    "This notebook provides some examples of cross-walking in descriptive epidemiological meta-regression.  In this context, cross-walking refers to mapping between two different, but related, quantities that have been measured.  For example, it is more accurate to directly measure blood pressure to test for hypertension, but it is cheaper to simply ask respondents if they have ever been told by a medical professional that they have high blood pressure.\n",
    "\n",
    "It is possible to cross-walk inside DisMod-MR, but if sufficent information is available cross-walking outside of DisMod-MR can provide more flexibility to you, the analyst.\n",
    "\n",
    "This notebook uses simulated data to compare simple approaches for cross-walking inside and outside of DisMod-MR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np, pandas as pd, dismod_mr\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set a random seed to ensure reproducible simulation results\n",
    "np.random.seed(123456)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simulate data\n",
    "n = 20\n",
    "\n",
    "data = dict(age=np.random.randint(0, 10, size=n)*10,\n",
    "            year=np.random.randint(1990, 2010, size=n))\n",
    "data = pd.DataFrame(data)\n",
    "\n",
    "# in the simulation, some data will be measured, and some will be self-reported\n",
    "# the self-reported data values will be shifted by N(-.25, .1) in log-space\n",
    "data['self_report'] = np.random.randint(2, size=n)\n",
    "data['beta_self_report'] = np.random.normal(-.25, .1, size=n)\n",
    "\n",
    "data['true_value'] = (.1 + .001 * data.age) + np.random.normal(0., .01, size=n)\n",
    "data['self_report_value'] = data.true_value * np.exp(data.beta_self_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 540x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's see the difference between the (simulated) true value and self-report value\n",
    "\n",
    "sns.lmplot('true_value', 'self_report_value', data, aspect=1.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to really see what is going on, it is better to look at residuals of some sort\n",
    "data['log_residual'] = np.log(data.true_value) - np.log(data.self_report_value)\n",
    "sns.residplot('true_value', 'log_residual', data, lowess=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['data_type'] = 'p'\n",
    "\n",
    "data['age_start'] = data.age\n",
    "data['age_end'] = data.age+10\n",
    "\n",
    "# for prettier display, include jittered age near midpoint of age interval\n",
    "data['jittered_age'] = .5*(data.age_start + data.age_end) + np.random.normal(size=n)\n",
    "\n",
    "# keep things simple, no spatial random effects, no sex effect\n",
    "data['area'] = 'all'\n",
    "data['sex'] = 'total'\n",
    "\n",
    "# quantification of uncertainty that says these numbers are believed to be quite precise\n",
    "data['standard_error'] = -99\n",
    "data['upper_ci'] = np.nan\n",
    "data['lower_ci'] = np.nan\n",
    "data['effective_sample_size'] = 1.e8\n",
    "\n",
    "\n",
    "def new_model(data):\n",
    "    # build the dismod_mr model\n",
    "    dm = dismod_mr.data.ModelData()\n",
    "\n",
    "    # set simple model parameters, for decent, fast computation\n",
    "    dm.set_knots('p', [0,100])\n",
    "    dm.set_level_bounds('p', lower=0, upper=1)\n",
    "    dm.set_level_value('p', age_before=0, age_after=100, value=0)\n",
    "    dm.set_heterogeneity('p', value='Slightly')\n",
    "    dm.set_effect_prior('p', cov='x_sex', value=dict(dist='Constant', mu=0))\n",
    "    \n",
    "    # copy data into model \n",
    "    dm.input_data = data.copy()\n",
    "    \n",
    "    return dm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is what things would look like if we had all measured values (`true_value`) or all self-reported values (`self_report_value`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 661.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = pd.melt(data, id_vars=['jittered_age'], value_vars=['true_value', 'self_report_value'])\n",
    "sns.lmplot('jittered_age', 'value', t, hue='variable', aspect=1.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But often the dataset we find has no overlap, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "data['value'] = data.true_value\n",
    "dismod_mr.plot.data_bars(data[data.self_report==0], color=dismod_mr.plot.colors[0], label='Measured')\n",
    "\n",
    "data['value'] = data.self_report_value\n",
    "dismod_mr.plot.data_bars(data[data.self_report==1], color=dismod_mr.plot.colors[1], label='Self-report')\n",
    "plt.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we had access to the true value, we would just fit that, and get nice results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_p_x_sex x_sex {'dist': 'Constant', 'mu': 0}\n",
      "finding initial values\n",
      ". . . \n",
      "finding MAP estimate\n",
      "\n",
      "finding step covariances estimate\n",
      "\n",
      "resetting initial values (1)\n",
      ". . . \n",
      "resetting initial values (2)\n",
      "\n",
      "mare: 0.03\n",
      "sampling from posterior\n",
      "\n",
      "CPU times: user 20.6 s, sys: 60 ms, total: 20.6 s\n",
      "Wall time: 20.6 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['value'] = data.true_value\n",
    "dm = new_model(data)\n",
    "\n",
    "dm.setup_model('p', rate_model='neg_binom')\n",
    "%time dm.fit(how='mcmc', iter=2000, burn=1000, thin=1)\n",
    "dm.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we have measurements of both the measured value and the self-reported for many data points, we could do a cross-walk outside of dismod and adjust all of the data with only self-reported measurements before building the DisMod-MR model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mIEiJi+TqMW17L76Ur3Wsg4WKOoD9z9sFRISG8MOpg5yOIl7Uv0c3pg/vyWf7j/De5v1Ox5EuoKIOUMuLKlm89QC3XNCfHnFRTscRLzu7bzJj+ySxrKiStSWHnY4jPqaiDkDNLa3c/eZWeidF82/j+zgdR3zkouE96Zcay2vr9lJcqZkggUxFHYBeXFPKtgNH+MmFZ2g6XgALDTFcPSabxJhwnltVwmHNBAlYKuoAU32siQcXb2dsnySmnqltnQJddEQo17XPBHlWM0ECloo6wPz+g0IO1zXy84uGaDpekEiNi2TWmCzNBAlgKuoAsrOilmc+KebKvN6cqduMg8qAHnFfzAR5v+CA03HEy7TWRwD5n7cKiAoP5c4pgTEd74VVu52O4FfG9UmirOoYS7dVkJEQrTVBAkiHR9TGmN7GmCXGmAJjzBZjzG1dEUw656PtFXzwWTm3XtCf1DjtKB6MjDHMGNGT3t2jeXltKWXVx5yOJF7iyamPZuBOa+0ZwDjgFmOMFjR2keaWVn69cCvZyTHcMD7H6TjioLDQEK4Zm01UeCjPrSyhrqHZ6UjiBR0WtbW2zFr7afvjI0AB0MvXwcRzL6zeTWF5LT+Zdoa21xLio8O5Zmw2NfXN2h0mQHTqYqIxJgcYBazyRRjpvKq6Rh5cvJ1z+iUzRZvVSruspBguGdGToopa3tui28z9ncdFbYzpBvwNuN1aW3OC5+cYY/KNMfkVFRXezCgn8bv3C6k51qTpePIv8nKSGNc3mY+LKnl13R6n48hp8KiojTHhtJX089baV070GmvtPGttnrU2LzVV2wV1haLyI8xfWcJVY7K0Wa2c0PRhGfRJieVHf9vEpj3VTseRU+TJrA8DPAkUWGsf9H0k8dQ9bxUQExHKnZMHOh1FXCo0xDBrTBYp3SKZMz+fiiMNTkeSU+DJEfV4YDZwgTFmffvHNB/nkg4s2VbO0m0V3Pb1ASR303Q8+WrdIsOYOzuXw3WN3PL8pzS1tDodSTrJk1kfH1trjbV2uLV2ZPvH210RTk6sqaWVexZupU9KLNedneN0HPEDZ/ZK4P++NZzVxYe4+82tTseRTtKdiX7ouZUl7Kg4yhPX5RERplUAxDOXjOzFln01zPtoJ0N7xnPVmCynI4mH9FvuZw4fbeR37xcyYUAKXz+jh9NxxM/cNXUwEwak8PPXN2vDAT+iovYzD72/nSP1TfxsuqbjSeeFhhgemTWKjIRovvvcWg7U1DsdSTygovYj2w8c4flVu7lmbDaD0uOcjiN+KjEmgj9dl8fRhmZumr+Weq1h7Xoqaj9hreXXC7cSGxHK9zUdT07ToPQ4HrxiBOtLq/jv1zdjtYa1q6mo/cT7BeUsK6zk9kkDSYqNcDqOBICpZ2bwnxf0Z0H+HuavLHE6jpyEitoP1De1cPfCLQzo0Y3ZZ2c7HUcCyO2TBjLpjB7c/eZWVu486HQc+Qoqaj8w76OdlB46xq9mDCU8VG+ZeE9IiOGhK0eSnRzDzc9/yt4qrWHtRvqtd7k9h+v449Iipg1L55z+KU7HkQAUFxXOvOvyaGpuZc6z+Rxr1MVFt1FRu9y9bxcA8NPp2qtBfKdfajcenjWSrWU1/OiVjbq46DIqahdbXlTJ25v2c/N5/emVGO10HAlwFwxO4wdTBvH6+n08sWyX03HkOCpql2pqaeWXb2yhd1I0cyb2dTqOBImbz+vHtGHp3PdOAcsKta68W6ioXerZFSUUltfy8+lDiArX9lrSNYwx/HbmCAamxfG9F9ax+2Cd05EEFbUr7a+u56HF25k4MJXJ2l5LulhsZBjzZucB8J1n8zmqDXIdp6J2oV+8sZmmllZ+fclQrechjshKjuHRq0dRWH6EH/x1gy4uOkxF7TKLtuznvS0HuG3SALKTY52OI0FswoBUfnzhGbyzeT+PfljkdJygpvWoXaS2oZlfvLGFwelxfGeCLiCK826c0IeCshoeWLydfj26MW1YhtORgpKOqF3k/ve2sb+mnnsvG6Y7EMUVjDHce9kwcrO7c8eC9WzcU+V0pKCkNnCJDaVVPLOimNnjsjkrq7vTcUS+EBUeytzZuaR0i+TGZ/Ipq9Zt5l1NRe0CTS2t/OiVTfSIi+SH3xjkdByRf5HSLZInrx9NXWMLNz6TT12jZoJ0JRW1Czz58S4Kymr41YyhxEWFOx1H5IQGpcfxyKxRFJTVcPuL62lt1UyQrqKidti2/Ud4cNF2pgxJ4xtD052OI3JS5w/uwc+mD2HR1gP8dtE2p+MEDc36cFBjcyvff2k9cVFh3HvZMM2ZFr/w7fE57Kio5bGlO+iX2o2ZuZlORwp4OqJ20O8/KGRrWQ33XTaMlG6RTscR8Ygxhl/OGMr4/sn8+JWNrN51yOlIAa/DojbGPGWMKTfGbO6KQMFibclh/ri0iMtzM5miUx7iZ8JDQ/jj1bn0Tophzvx8isprnY4U0Dw5on4amOrjHEGlrrGZOxesJyMhmv++WOtMi39KiAnn6RvGEBYSwvVPreZATb3TkQJWh0Vtrf0I0N82XnTv2wWUHKrjgStGaJaH+LWs5Bie/vZoquoauf6p1dTUNzkdKSB57Ry1MWaOMSbfGJNfUaF1bL/Kkm3lPLdyNzee24dxfZOdjiNy2s7slcDjs3MpKq/lpmfX0tCsrby8zWtFba2dZ63Ns9bmpaameuvLBpSy6mPc8dJ6BqfHcecU3dgigWPCgFR+e/lwVuw8yB0LNmiOtZdpel4XaW5p5dYX1tHQ3MofrjlLmwFIwPnmqEzKaxq4753PSImN4JcztEyvt6iou8j9i7aTX3KY3105kn6p3ZyOI+ITcyb2pbK2gT8t20VUeCg/unCwytoLOixqY8xfgPOAFGPMHuAX1tonfR0skLy9qYzH/76DWWOyuHRUL6fjiPiMMYafTDuD+qZW5n60k8jwUO6YPNDpWH6vw6K21s7qiiCBatv+th0yRmUl8ssZmoongc8Yw69mDKWhuYXff1BIVHgIN5/X3+lYfk2nPnyoqq6ROfPziY0M4/Frc4kM03lpCQ4hIYb7LhtOQ3Mrv3l3G5Fhofz7uX2cjuW3VNQ+0tDcwpxn11JWVc9f5owjLT7K6UgiXSo0xPDA5SNobG7l1wu3YoB/U1mfEq314QPWWu56eSOriw/x28uHk5utjQAkOIWFhvDwVaOYOjSduxdu5eH3C7VR7ilQUfvAA4u289r6ffxgykAuGamLhxLcIsJCePTqUczMzeSh97dzz1sFKutO0qkPL3ti2U4eXVLErDG9ueV8XUARgbYj6998azjdIsN48uNdHKlv4r7LhhMaoql7nlBRe9GCNaXc81YB04dlcM+lWl9a5HghIYZfXDyEhOhwHv6gkNqGZh68YqRu/vKAitpLXl67h7te2cjEgak8dOVIHSmInIAxhu9PHkhcVBj3vFVAWfVK5s7OpUecLrafjM5Re8GCNaX88OUNjO+Xwtxrc4kI0/9WkZO5cUJfHrvmLD4rO8Kljy5n895qpyO5mhrlND29fBf/9beNTByQyhPX5xEdoT/jRDxx4bAM/vrds7HA5Y+v4N3NZU5Hci0V9Smy1vJ/737GL9/cyuQhacydnatzbSKddGavBF7/3ngGZ8Tx3ec+5fcfFGrlvRNQUZ+C+qYW7liwgceW7uCasVk8fq1KWuRU9YiL4i/fGcdlo3rx4OLtXP/n1VTWNjgdy1VU1J20v7qeK+et5NV1e/nBlIHcc+mZunAocpqiwkN54IoR3PvNYazedYhpDy9jWaE2IPmciroTPtlRycWPfkzRgSPMnZ3L9y4YoCl4Il5ijOHqsVm8dst44qPDmf3kan7+2mbqGpudjuY4FbUHmltaeWDRNq55YhVxkWG8cvN4vqGdw0V84oyMeBbeei7/fm4fnltVwtTfLeOj7cF9dK2i7sC2/Uf41mOf8MiHRVyem8mbt57LoPQ4p2OJBLSo8FB+ftEQ/vKdcYSFGK57ajW3vbguaHc6V1F/hfqmFh5avJ2LHlnGnsPHePTqUfxm5ghiIwPjHqH3Cw64/mv6IqMvv6cTeT310OLtTkc4JeP6JvP2bRO47esDeGfTfs6/fymPfFBIfVNwbaCrov4Say3vbCpj0oN/5+EPCpk2LIPFd3yNi4b3dDqaV334Wbnrv6YvMvryezqR11MPf1DodIRTFhUeyvcnD2TxHROZOCCVBxZvZ+JvljB/RTGNza1Ox+sSgXF46AXWWpYXHeSBxdtYt7uKwelxvPCdsZzTL8XpaCICZCfH8vjsXFbtPMj9i7bx89e38NjSHVx/Tg5Xjc4iISbc6Yg+E/RF3dJqWbz1AE8s20l+yWF6JkRx32XDuDw3k7BQ/cEh4jZj+yaz4Kaz+aiwkseX7uC+dz7jd+8XMjM3kxvG5wTk5tFBW9TlNfW8/OkeXlxdyu5DdWR2j+buS4Zy5eje2jJLxOWMMXxtYCpfG5jKln3V/Hl5MS+tKWX+yhLG909mxoieTB2aETBH2UFV1NV1Tby3dT9vbypjWWElLa2WMX2S+PGFg5kyNF03roj4oaE9E7j/8hHcNXUwz68q4dV1e7nrb5v42WubmTgglYtGZHDBoDS/Lu2ALuqG5hY+LalieVElHxdVsnFPFa0WMrtHM2diXy7PzaRvAP6ZJBKMUuMiuX3SQG77+gA27a3mzQ37eGtjGR98Vo4xcGbPBM7pn8w5/VIYndOdmAj/qT//SdqBppZWispr2bqvhq1lNWzZV8360irqm1oJDTGMyEzglvP7M3lIGsN6JeiOQpEAZYxheGYiwzMT+fGFZ7Cu9DDLCiv5pOggT328i7l/30lYiGFAWhxDe8YzJCOeoT3jGZwRT0K0O4+6PSpqY8xU4GEgFHjCWvu/Pk31JfVNLVTVNXG4rpHDRxupPNrI3sPH2HO4jj3t/5YeOkZjS9tUnciwEAZnxHPV6CzO7Z/C2L5JxEW58w0QEd8JCTHkZieRm53E7ZOgrrGZ/OLDrNx5kM37ali6rZyX1+754vXdY8LJSoohMymGrKQYeiZGkxIbQVJsBMndIkmOjSA+OrzLT5N2WNTGmFDgD8BkYA+wxhjzhrV2q7fDXPbH5dQ2NNPQ3Ep9U8sX/9Y3nXiuZPeYcDK7xzAwLY7JQ9IZ0jOeIRlx5CTHasaGiPyLmIgwJg5MZeLAVKBtWm7FkQa27Kvhs/1HKD1cR+mhOrbsrea9zftp/oolVyPCQoiJCCUmPJToiFDCQ0MIDTEkd4vk2X8b4/XcnhxRjwGKrLU7AYwxLwKXAF4v6rT4KHpYiAoPITIstO3f8FASosPpHhNB95hwEmMiSO4WQc/EaLoFyF2CIuIMYww94qPoER/F+YN7/NNzLa2WytoGDtY2cuhoIwePtj0+Ut9MXVMzxxpbqGts4VhjC00trbS0WuKifNNJpqNt240xM4Gp1tob2z+fDYy11n7vS6+bA8xp/3QQsM37cb9SClDZhd+vK2hM/iMQxxWIYwJ3jyvbWpt6oic8qf8TnYz5l3a31s4D5nUymFcYY/KttXlOfG9f0Zj8RyCOKxDHBP47Lk9O5O4Beh/3eSawzzdxRETkyzwp6jXAAGNMH2NMBHAV8IZvY4mIyOc6PPVhrW02xnwPeI+26XlPWWu3+DxZ5zhyysXHNCb/EYjjCsQxgZ+Oq8OLiSIi4ixNNhYRcTkVtYiIy/lNURtjphpjthljiowxPzrB85HGmJfan19ljMnp+pSd58G4JhpjPjXGNLfPaXc9D8Z0hzFmqzFmozHmA2NMthM5O8uDcX3XGLPJGLPeGPOxMWaIEzk7o6MxHfe6mcYYa4zxi6ltHrxXNxhjKtrfq/XGmBudyOkxa63rP2i7iLkD6AtEABuAIV96zc3A4+2PrwJecjq3l8aVAwwHngVmOp3ZS2M6H4hpf/wfAfRexR/3eAbwrtO5T3dM7a+LAz4CVgJ5Tuf20nt1A/Co01k9/fCXI+ovbmO31jYCn9/GfrxLgGfaH78MfN24f4m8DsdlrS221m4E/GVzOE/GtMRaW9f+6Ura5ua7nSfjqjnu01hOcGOYy3jyewXwa+A3gL9sAe7puPyGvxR1L6D0uM/3tP+3E77GWtsMVAPJXZLu1HkyLiZXEOEAAAHSSURBVH/T2TH9O/COTxN5h0fjMsbcYozZQVux/WcXZTtVHY7JGDMK6G2tXdiVwU6Tpz+D32o//fayMab3CZ53DX8pak9uY/foVneX8cfMHfF4TMaYa4E84Lc+TeQdni6l8AdrbT/gLuBnPk91ek46JmNMCPAQcGeXJfIOT96rN4Eca+1w4H3+8de4K/lLUXtyG/sXrzHGhAEJwKEuSXfqAvH2fI/GZIyZBPwUmGGtbeiibKejs+/Vi8ClPk10+joaUxxwJrDUGFMMjAPe8IMLih2+V9bag8f93P0JyO2ibKfEX4rak9vY3wCub388E/jQtl81cLFAvD2/wzG1/zk9l7aSLncg46nwZFwDjvt0OlDYhflOxUnHZK2tttamWGtzrLU5tF1PmGGtzXcmrsc8ea8yjvt0BlDQhfk6z+mrmZ24kjsN2E7b1dyftv+3u2n7wQGIAv4KFAGrgb5OZ/bSuEbTdoRwFDgIbHE6sxfG9D5wAFjf/vGG05m9NK6HgS3tY1oCDHU68+mO6UuvXYofzPrw8L26r/292tD+Xg12OvPJPnQLuYiIy/nLqQ8RkaClohYRcTkVtYiIy6moRURcTkUtIuJyKmoREZdTUYuIuNz/AzVlBFiST/suAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# simulate 20% portion of data with both measured and self-reported values\n",
    "\n",
    "data['measured_both'] = np.random.randint(5, size=n) == 0\n",
    "\n",
    "log_diff = np.log(data.true_value[data.measured_both]) - np.log(data.self_report_value[data.measured_both])\n",
    "sns.distplot(log_diff, rug=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use exp of mean of log(difference) to cross-walk self-reported data before building DisMod-MR model\n",
    "\n",
    "data['cross_walked'] = data.self_report_value * np.exp(log_diff.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_p_x_sex x_sex {'dist': 'Constant', 'mu': 0}\n",
      "finding initial values\n",
      ". . . \n",
      "finding MAP estimate\n",
      "\n",
      "finding step covariances estimate\n",
      "\n",
      "resetting initial values (1)\n",
      ". . . \n",
      "resetting initial values (2)\n",
      "\n",
      "mare: 0.04\n",
      "sampling from posterior\n",
      "\n",
      "CPU times: user 20.6 s, sys: 12 ms, total: 20.6 s\n",
      "Wall time: 20.6 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['value'] = np.where(data.self_report==1, data.cross_walked, data.true_value)\n",
    "dm = new_model(data)\n",
    "\n",
    "dm.setup_model('p', rate_model='neg_binom')\n",
    "%time dm.fit(how='mcmc', iter=2000, burn=1000, thin=1)\n",
    "dm.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this simple model of a cross-walk, it would be equivalent to include the mean of the log-differences an informative prior on the self-report effect coefficient, which can be more convenient than shifting the data. Note the sign of the effect prior:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_p_x_self_report x_self_report {'dist': 'Constant', 'mu': -0.2512521670410081}\n",
      "using stored FE for beta_p_x_sex x_sex {'dist': 'Constant', 'mu': 0}\n",
      "finding initial values\n",
      ". . . \n",
      "finding MAP estimate\n",
      "\n",
      "finding step covariances estimate\n",
      "\n",
      "resetting initial values (1)\n",
      ". . . \n",
      "resetting initial values (2)\n",
      "\n",
      "mare: 0.04\n",
      "sampling from posterior\n",
      "\n",
      "CPU times: user 20.6 s, sys: 84 ms, total: 20.7 s\n",
      "Wall time: 20.6 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['value'] = np.where(data.self_report==1, data.self_report_value, data.true_value)\n",
    "data['x_self_report'] = data.self_report\n",
    "\n",
    "dm = new_model(data)\n",
    "dm.set_effect_prior('p', 'x_self_report',\n",
    "                    dict(dist='Constant', mu=-log_diff.mean()))\n",
    "\n",
    "\n",
    "dm.setup_model('p', rate_model='neg_binom')\n",
    "%time dm.fit(how='mcmc', iter=2000, burn=1000, thin=1)\n",
    "dm.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing the cross-walk completely outside of DisMod-MR, and adjusting the data before building the model allows for more complicated cross-walk modeling, however, such as different cross-walk factors based on age or other relevant demographic variables.  (Of course, this requires more cross-walk-able data, and is a topic that we will not explore further here.)\n",
    "\n",
    "If we have little or no studies that have reported both outcomes, we can cross-walk with DisMod-MR fixed effects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_p_x_sex x_sex {'dist': 'Constant', 'mu': 0}\n",
      "finding initial values\n",
      ". . . \n",
      "finding MAP estimate\n",
      "\n",
      "finding step covariances estimate\n",
      "\n",
      "resetting initial values (1)\n",
      ". . . \n",
      "resetting initial values (2)\n",
      "\n",
      "mare: 0.05\n",
      "sampling from posterior\n",
      "\n",
      "CPU times: user 23.7 s, sys: 88 ms, total: 23.8 s\n",
      "Wall time: 23.8 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['value'] = np.where(data.self_report==1, data.self_report_value, data.true_value)\n",
    "data['x_self_report'] = data.self_report\n",
    "\n",
    "dm = new_model(data)\n",
    "dm.setup_model('p', rate_model='neg_binom')\n",
    "\n",
    "%time dm.fit(how='mcmc', iter=2000, burn=1000, thin=1)\n",
    "dm.plot()\n",
    "\n",
    "data['value'] = data.true_value\n",
    "dismod_mr.plot.data_bars(data[data.x_self_report==0], color=dismod_mr.plot.colors[0], label='Measured')\n",
    "\n",
    "data['value'] = data.self_report_value\n",
    "dismod_mr.plot.data_bars(data[data.x_self_report==1], color=dismod_mr.plot.colors[1], label='Self-report')\n",
    "plt.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f260f1b6ef0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dm.vars['p']['beta'][0].trace())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is sometimes helpful to include informative priors on the effect coefficients, for example if it is known that the self-report outcome is always less than the direct measurement.\n",
    "\n",
    "DisMod-MR has three distributions that may be used as an informative prior on a \n",
    "\n",
    "This can also be used to combine a cross-walk outside of DisMod-MR with a tune-up inside of the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using stored FE for beta_p_x_self_report x_self_report {'dist': 'TruncatedNormal', 'mu': -0.2512521670410081, 'sigma': 0.08458627167990279, 'lower': -0.25, 'upper': -0.2}\n",
      "using stored FE for beta_p_x_sex x_sex {'dist': 'Constant', 'mu': 0}\n",
      "finding initial values\n",
      ". . . \n",
      "finding MAP estimate\n",
      "\n",
      "finding step covariances estimate\n",
      "\n",
      "resetting initial values (1)\n",
      ". . . \n",
      "resetting initial values (2)\n",
      "\n",
      "mare: 0.05\n",
      "sampling from posterior\n",
      "\n",
      "CPU times: user 19.9 s, sys: 64 ms, total: 19.9 s\n",
      "Wall time: 19.9 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['value'] = np.where(data.self_report==1, data.self_report_value, data.true_value)\n",
    "\n",
    "dm = new_model(data)\n",
    "dm.set_effect_prior('p', 'x_self_report',\n",
    "                    dict(dist='TruncatedNormal', mu=-log_diff.mean(), sigma=log_diff.std(), lower=-.25, upper=-.2))\n",
    "\n",
    "\n",
    "dm.setup_model('p', rate_model='neg_binom')\n",
    "%time dm.fit(how='mcmc', iter=2000, burn=1000, thin=1)\n",
    "dm.plot()\n",
    "\n",
    "data['value'] = data.true_value\n",
    "dismod_mr.plot.data_bars(data[data.x_self_report==0], color=dismod_mr.plot.colors[0], label='Measured')\n",
    "\n",
    "data['value'] = data.self_report_value\n",
    "dismod_mr.plot.data_bars(data[data.x_self_report==1], color=dismod_mr.plot.colors[1], label='Self-report')\n",
    "plt.legend(loc='upper left');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f260e6eaa90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(dm.vars['p']['beta'][0].trace())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Jul 23 13:45:18 PDT 2019\r\n"
     ]
    }
   ],
   "source": [
    "!date"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dismod_mr",
   "language": "python",
   "name": "dismod_mr"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
